Milad Alshomary


2022

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Identifying the Human Values behind Arguments
Johannes Kiesel | Milad Alshomary | Nicolas Handke | Xiaoni Cai | Henning Wachsmuth | Benno Stein
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper studies the (often implicit) human values behind natural language arguments, such as to have freedom of thought or to be broadminded. Values are commonly accepted answers to why some option is desirable in the ethical sense and are thus essential both in real-world argumentation and theoretical argumentation frameworks. However, their large variety has been a major obstacle to modeling them in argument mining. To overcome this obstacle, we contribute an operationalization of human values, namely a multi-level taxonomy with 54 values that is in line with psychological research. Moreover, we provide a dataset of 5270 arguments from four geographical cultures, manually annotated for human values. First experiments with the automatic classification of human values are promising, with F1-scores up to 0.81 and 0.25 on average.

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The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments
Milad Alshomary | Roxanne El Baff | Timon Gurcke | Henning Wachsmuth
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

An audience’s prior beliefs and morals are strong indicators of how likely they will be affected by a given argument. Utilizing such knowledge can help focus on shared values to bring disagreeing parties towards agreement. In argumentation technology, however, this is barely exploited so far. This paper studies the feasibility of automatically generating morally framed arguments as well as their effect on different audiences. Following the moral foundation theory, we propose a system that effectively generates arguments focusing on different morals. In an in-depth user study, we ask liberals and conservatives to evaluate the impact of these arguments. Our results suggest that, particularly when prior beliefs are challenged, an audience becomes more affected by morally framed arguments.

2021

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Counter-Argument Generation by Attacking Weak Premises
Milad Alshomary | Shahbaz Syed | Arkajit Dhar | Martin Potthast | Henning Wachsmuth
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Generating Informative Conclusions for Argumentative Texts
Shahbaz Syed | Khalid Al Khatib | Milad Alshomary | Henning Wachsmuth | Martin Potthast
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Belief-based Generation of Argumentative Claims
Milad Alshomary | Wei-Fan Chen | Timon Gurcke | Henning Wachsmuth
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

In this work, we argue that augmenting argument generation technology with the ability to encode beliefs is of twofold. First, it gives more control on the generated arguments leading to better reach for audience. Second, it is one way of modeling the human process of synthesizing arguments. Therefore, we propose the task of belief-based claim generation, and study the research question of how to model and encode a user’s beliefs into a generated argumentative text. To this end, we model users’ beliefs via their stances on big issues, and extend state of the art text generation models with extra input reflecting user’s beliefs. Through an automatic evaluation we show empirical evidence of the applicability to encode beliefs into argumentative text. In our manual evaluation, we highlight that the low effectiveness of our approach stems from the noise produced by the automatic collection of bag-of-words, which was mitigated by removing this noise. The finding of this paper lays the ground work to further investigate the role of beliefs in generating better reaching arguments.

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Assessing the Sufficiency of Arguments through Conclusion Generation
Timon Gurcke | Milad Alshomary | Henning Wachsmuth
Proceedings of the 8th Workshop on Argument Mining

The premises of an argument give evidence or other reasons to support a conclusion. However, the amount of support required depends on the generality of a conclusion, the nature of the individual premises, and similar. An argument whose premises make its conclusion rationally worthy to be drawn is called sufficient in argument quality research. Previous work tackled sufficiency assessment as a standard text classification problem, not modeling the inherent relation of premises and conclusion. In this paper, we hypothesize that the conclusion of a sufficient argument can be generated from its premises. To study this hypothesis, we explore the potential of assessing sufficiency based on the output of large-scale pre-trained language models. Our best model variant achieves an F1-score of .885, outperforming the previous state-of-the-art and being on par with human experts. While manual evaluation reveals the quality of the generated conclusions, their impact remains low ultimately.

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Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
Milad Alshomary | Timon Gurcke | Shahbaz Syed | Philipp Heinisch | Maximilian Spliethöver | Philipp Cimiano | Martin Potthast | Henning Wachsmuth
Proceedings of the 8th Workshop on Argument Mining

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis Shared Task, colocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.

2020

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Target Inference in Argument Conclusion Generation
Milad Alshomary | Shahbaz Syed | Martin Potthast | Henning Wachsmuth
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In argumentation, people state premises to reason towards a conclusion. The conclusion conveys a stance towards some target, such as a concept or statement. Often, the conclusion remains implicit, though, since it is self-evident in a discussion or left out for rhetorical reasons. However, the conclusion is key to understanding an argument and, hence, to any application that processes argumentation. We thus study the question to what extent an argument’s conclusion can be reconstructed from its premises. In particular, we argue here that a decisive step is to infer a conclusion’s target, and we hypothesize that this target is related to the premises’ targets. We develop two complementary target inference approaches: one ranks premise targets and selects the top-ranked target as the conclusion target, the other finds a new conclusion target in a learned embedding space using a triplet neural network. Our evaluation on corpora from two domains indicates that a hybrid of both approaches is best, outperforming several strong baselines. According to human annotators, we infer a reasonably adequate conclusion target in 89% of the cases.

2019

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Modeling Frames in Argumentation
Yamen Ajjour | Milad Alshomary | Henning Wachsmuth | Benno Stein
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the “right” frame(s) to convince the audience to adopt the author’s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience’s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates’ topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.